The past decade has seen tremendous progress in the speed, quality and availability of automatic translation of natural languages. While automatic translation quality still regularly falls short of publication or near-publication quality, contemporary machine translation can deliver a level of quality that may boost the productivity of human translators by providing them with raw translations to work from, ensuring consistency in terminology, and fast access to terminological databases and databases of previous translations in the form of translation memories and bilingual concordances. Much research in the CL community in recent years has focused on improving fully automatic MT, but we still know comparatively little about how humans translate, and how to optimally organize human-machine interaction in computer-assisted translation. This workshop aims to provide a platform to discuss these issues and to present empirical results, data sets, case studies, and tools for computer-assisted translation and the study of cognitive processes in both fully human and computer-assisted human translation.